def extract_ft_force(self, window={}):
#print('ft force reporting')
if not window:
xftf(self.larch,
group=self.larch,
_larch=self._larch,
kmin=self.kmin_ft,
kmax=self.kmax_ft)
else:
window_type = window['window_type']
tapering = window['tapering']
r_weight = window['r_weight']
print('setting window')
xftf(self.larch,
group=self.larch,
_larch=self._larch,
kmin=self.kmin_ft,
kmax=self.kmax_ft,
window=window_type,
dk=tapering,
rweight=r_weight)
self.r = self.larch.r
self.chir = self.larch.chir
self.chir_mag = self.larch.chir_mag
self.chir_im = self.larch.chir_re
self.chir_re = self.larch.chir_im
#self.chir_pha = self.larch.chir_phas
self.kwin = self.larch.kwin
def calcFT(k, chi, kw, k_min, k_max, wind, dk_wind):
ft = larch_builtins._group(mylarch)
xftf(k,
chi,
group=ft,
kweight=kw,
kmin=k_min,
kmax=k_max,
window=wind,
dk=dk_wind,
_larch=mylarch)
return ft.r, ft.chir, ft.chir_mag, ft.chir_im
def calc_FT(k, chi, kmin_, kmax_, kweight_, FTwindow, delta_k):
ft = larch_builtins._group(mylarch)
xftf(k,
chi,
group=ft,
kweight=kweight_,
kmin=kmin_,
kmax=kmax_,
window=FTwindow,
dk=delta_k,
_larch=mylarch)
return ft.r, ft.chir_mag, ft.chir_im
def FT_F(self,
kmin=0,
kmax=None,
kweight=0,
dk=1,
dk2=None,
with_phase=False,
window='kaiser',
rmax_out=10,
nfft=2048,
kstep=0.05,
**kws):
""" forward XAFS Fourier transform, from chi(k) to chi(R), using
common XAFS conventions from larch.
Parameters:
-----------
rmax_out: highest R for output data (10 Ang)
kweight: exponent for weighting spectra by k**kweight
kmin: starting k for FT Window
kmax: ending k for FT Window
dk: tapering parameter for FT Window
dk2: second tapering parameter for FT Window
window: name of window type
nfft: value to use for N_fft (2048).
kstep: value to use for delta_k (0.05 Ang^-1).
with_phase: output the phase as well as magnitude, real, imag [False]
Returns:
---------
None -- outputs are written to supplied group.
Notes:
-------
Arrays written to output group:
kwin window function Omega(k) (length of input chi(k)).
r uniform array of R, out to rmax_out.
chir complex array of chi(R).
chir_mag magnitude of chi(R).
chir_re real part of chi(R).
chir_im imaginary part of chi(R).
chir_pha phase of chi(R) if with_phase=True
(a noticable performance hit)
Supports First Argument Group convention
(with group member names 'k' and 'chi')"""
args = dict(locals()) #; args.update(kws)
del args["self"], args["kws"]
if not (kmax): args["kmax"] = self.k[-1]
xafs.xftf(self, _larch=self._larch, **args)
return
def extract_ft(self):
if self.verbose:
print('ft reporting')
print(self.kmin_ft)
xftf(self.larch, group=self.larch, _larch=self._larch, kmin=self.kmin_ft, kmax=self.kmax)
self.r = self.larch.r
self.chir = self.larch.chir
self.chir_mag = self.larch.chir_mag
self.chir_im = self.larch.chir_re
self.chir_re = self.larch.chir_im
#self.chir_pha = self.larch.chir_pha
self.kmax_ft = self.kmax
self.kwin = self.larch.kwin
def run_autobk(Energy,Ut,E0,Rbkg,Kweight,k_min,k_max,fit_s,fit_e,nor_aE0_s,nor_aE0_e,pre_type):
exafs = larch_builtins._group(mylarch)
ft = larch_builtins._group(mylarch)
Pre_edge_kws = {'pre1':fit_s-E0,'pre2':fit_e-E0,'norm1':nor_aE0_s-E0,'norm2':nor_aE0_e-E0}
if pre_type == 2:
Pre_edge_kws['nvict']=4
else:
pass
autobk(Energy,mu=Ut,e0=E0,rbkg=Rbkg,kmin=k_min, kmax=k_max,kweight=Kweight,pre_edge_kws=Pre_edge_kws,group=exafs,_larch=mylarch)
#print larch_builtins._groupitems(exafs,mylarch)
xftf(exafs.k,exafs.chi,group=ft,kweight=3,kmin=3.0,kmax=12.0,_larch=mylarch)
k_l = math.sqrt(0.2626*(nor_aE0_s-E0))
k_h = math.sqrt(0.2626*(nor_aE0_e-E0))
mask = np.concatenate([np.zeros(len(exafs.k[0:find_near(exafs.k,k_min)])),
np.ones(len(exafs.k[find_near(exafs.k,k_min):find_near(exafs.k,k_max)+1])),
np.zeros(len(exafs.k[find_near(exafs.k,k_max)+1:]))])
return exafs.bkg, exafs.pre_edge, exafs.post_edge, exafs.chi, exafs.k, ft.r, ft.chir_mag, ft.chir_im
def paths_optimizations(self, number=0.01, verbose=False):
r"""
Paths optimizations using simpsons area calculation.
The calculation are used to perform
Inputs:
number (float): cut off percentage for paths_optimizations, the vals
is typically set at 1%
"""
total = 0
total_area = 0
contrib = []
contrib_area = []
for i in range(len(self.paths)):
self.best.k = self.ind_export_paths[2 * i, :]
self.best.chi = self.ind_export_paths[2 * i + 1, :]
xftf(self.best.k,
self.best.chi,
kmin=self.params['Kmin'],
kmax=self.params['Kmax'],
dk=4,
window='hanning',
kweight=self.params['kweight'],
group=self.best,
_larch=self.mylarch)
total += np.linalg.norm(self.best.chir_mag)
contrib.append(np.linalg.norm(self.best.chir_mag))
contrib_area.append(simps(self.best.chir_mag, self.best.r))
total_area += simps(self.best.chir_mag, self.best.r)
contrib_p = [i / total for i in contrib]
contrib_ap = [i / total_area for i in contrib_area]
if verbose:
print(
"Paths, Contrib Percentage (2-Norm), Contrib Percentage (Area)"
)
for i in range(len(self.paths)):
print(i + 1, contrib_p[i].round(3), contrib_ap[i].round(3))
#print(total)
new_path = (np.argwhere(np.array(contrib_ap) >= number)).flatten() + 1
print("New Paths")
print(new_path)
plt.bar(self.paths, height=contrib_ap)
plt.xticks(self.paths)
def FT_F(self, kmin=0, kmax=None, kweight=0, dk=1, dk2=None,
with_phase=False, window='kaiser', rmax_out=10, nfft=2048,
kstep=0.05, **kws ):
""" forward XAFS Fourier transform, from chi(k) to chi(R), using
common XAFS conventions from larch.
Parameters:
-----------
rmax_out: highest R for output data (10 Ang)
kweight: exponent for weighting spectra by k**kweight
kmin: starting k for FT Window
kmax: ending k for FT Window
dk: tapering parameter for FT Window
dk2: second tapering parameter for FT Window
window: name of window type
nfft: value to use for N_fft (2048).
kstep: value to use for delta_k (0.05 Ang^-1).
with_phase: output the phase as well as magnitude, real, imag [False]
Returns:
---------
None -- outputs are written to supplied group.
Notes:
-------
Arrays written to output group:
kwin window function Omega(k) (length of input chi(k)).
r uniform array of R, out to rmax_out.
chir complex array of chi(R).
chir_mag magnitude of chi(R).
chir_re real part of chi(R).
chir_im imaginary part of chi(R).
chir_pha phase of chi(R) if with_phase=True
(a noticable performance hit)
Supports First Argument Group convention
(with group member names 'k' and 'chi')"""
args=dict(locals())#; args.update(kws)
del args["self"],args["kws"]
if not(kmax): args["kmax"]=self.k[-1]
xafs.xftf(self, _larch=self._larch, **args)
return
def read_athena(filename, match=None, do_preedge=True, do_bkg=True, do_fft=True, use_hashkey=False, _larch=None):
"""read athena project file
returns a Group of Groups, one for each Athena Group in the project file
Arguments:
filename (string): name of Athena Project file
match (sring): pattern to use to limit imported groups (see Note 1)
do_preedge (bool): whether to do pre-edge subtraction [True]
do_bkg (bool): whether to do XAFS background subtraction [True]
do_fft (bool): whether to do XAFS Fast Fourier transform [True]
use_hashkey (bool): whether to use Athena's hash key as the
group name instead of the Athena label [False]
Returns:
group of groups each named according the label used by Athena.
Notes:
1. To limit the imported groups, use the pattern in `match`,
using '*' to match 'all' '?' to match any single character,
or [sequence] to match any of a sequence of letters. The match
will always be insensitive to case.
3. do_preedge, do_bkg, and do_fft will attempt to reproduce the
pre-edge, background subtraction, and FFT from Athena by using
the parameters saved in the project file.
2. use_hashkey=True will name groups from the internal 5 character
string used by Athena, instead of the group label.
Example:
1. read in all groups from a project file:
cr_data = read_athena('My Cr Project.prj')
2. read in only the "merged" data from a Project, and don't do FFT:
zn_data = read_athena('Zn on Stuff.prj', match='*merge*', do_fft=False)
"""
from larch_plugins.xafs import pre_edge, autobk, xftf
if not os.path.exists(filename):
raise IOError("%s '%s': cannot find file" % (ERR_MSG, filename))
try:
fh = GzipFile(filename)
lines = [bytes2str(t) for t in fh.readlines()]
fh.close()
except:
raise ValueError("%s '%s': invalid gzip file" % (ERR_MSG, filename))
athenagroups = []
dat = {"name": ""}
Athena_version = None
vline = lines.pop(0)
if "Athena project file -- Demeter version" not in vline:
raise ValueError("%s '%s': invalid Athena File" % (ERR_MSG, filename))
major, minor, fix = "0", "0", "0"
try:
vs = vline.split("Athena project file -- Demeter version")[1]
major, minor, fix = vs.split(".")
except:
raise ValueError("%s '%s': cannot read version" % (ERR_MSG, filename))
if int(minor) < 9 or int(fix[:2]) < 21:
raise ValueError("%s '%s': file is too old to read" % (ERR_MSG, filename))
for t in lines:
if t.startswith("#") or len(t) < 2:
continue
key = t.split(" ")[0].strip()
key = key.replace("$", "").replace("@", "")
if key == "old_group":
dat["name"] = perl2json(t)
elif key == "[record]":
athenagroups.append(dat)
dat = {"name": ""}
elif key == "args":
dat["args"] = perl2json(t)
elif key in ("x", "y", "i0"):
dat[key] = np.array([float(x) for x in perl2json(t)])
if match is not None:
match = match.lower()
out = Group()
out.__doc__ = """XAFS Data from Athena Project File %s""" % (filename)
for dat in athenagroups:
label = dat["name"]
this = Group(
athena_id=label, energy=dat["x"], mu=dat["y"], bkg_params=Group(), fft_params=Group(), athena_params=Group()
)
if "i0" in dat:
this.i0 = dat["i0"]
if "args" in dat:
for i in range(len(dat["args"]) // 2):
key = dat["args"][2 * i]
val = dat["args"][2 * i + 1]
if key.startswith("bkg_"):
setattr(this.bkg_params, key[4:], val)
elif key.startswith("fft_"):
setattr(this.fft_params, key[4:], val)
elif key == "label":
this.label = val
if not use_hashkey:
label = this.label
else:
setattr(this.athena_params, key, val)
this.__doc__ = """Athena Group Name %s (key='%s')""" % (label, dat["name"])
olabel = fix_varname(label)
if match is not None:
if not fnmatch(olabel.lower(), match):
continue
if do_preedge or do_bkg:
pars = this.bkg_params
pre_edge(
this,
_larch=_larch,
e0=float(pars.e0),
pre1=float(pars.pre1),
pre2=float(pars.pre2),
norm1=float(pars.nor1),
norm2=float(pars.nor2),
nnorm=float(pars.nnorm) - 1,
make_flat=bool(pars.flatten),
)
if do_bkg and hasattr(pars, "rbkg"):
autobk(
this,
_larch=_larch,
e0=float(pars.e0),
rbkg=float(pars.rbkg),
kmin=float(pars.spl1),
kmax=float(pars.spl2),
kweight=float(pars.kw),
dk=float(pars.dk),
clamp_lo=float(pars.clamp1),
clamp_hi=float(pars.clamp2),
)
if do_fft:
pars = this.fft_params
kweight = 2
if hasattr(pars, "kw"):
kweight = float(pars.kw)
xftf(
this,
_larch=_larch,
kmin=float(pars.kmin),
kmax=float(pars.kmax),
kweight=kweight,
window=pars.kwindow,
dk=float(pars.dk),
)
setattr(out, olabel, this)
return out
def larch_init(CSV_sub, params):
r"""
Larch initialization for data analysis
Inputs:
CSV_sub (str): files location of the data files (CSV/XMU)
params (dics): dicts contain all parameters
"""
global intervalK
global best
global KMIN
global KMAX
global KWEIGHT
global g
#
Kmin = params['Kmin']
Kmax = params['Kmax']
deltak = params['deltak']
BIG = int(Kmax / deltak)
SMALL = int(Kmin / deltak)
MID = int(BIG - SMALL + 1)
RBKG = params['rbkg']
KWEIGHT = params['kweight']
KMIN = Kmin
KMAX = Kmax
BKGKW = params['bkgkw'] # cu = 1 hfal2 = 2.0
BKGKMAX = params['bkgkmax'] # cu = 25, hfal2 = 15
# print(base)
CSV_PATH = os.path.join(base, CSV_sub)
g = read_ascii(CSV_PATH)
best = read_ascii(CSV_PATH, )
sumgroup = read_ascii(CSV_PATH)
# back ground subtraction using autobk
# data kweight
try:
g.chi
except AttributeError:
autobk(g, rbkg=RBKG, kweight=BKGKW, kmax=BKGKMAX, _larch=mylarch)
autobk(best, rbkg=RBKG, _larch=mylarch)
autobk(sumgroup, rbkg=RBKG, _larch=mylarch)
intervalK = (np.linspace(SMALL, BIG, MID)).tolist()
'''chang'''
xftf(g.k,
g.chi,
kmin=KMIN,
kmax=KMAX,
dk=4,
window='hanning',
kweight=KWEIGHT,
group=g,
_larch=mylarch)
xftf(best.k,
best.chi,
kmin=KMIN,
kmax=KMAX,
dk=4,
window='hanning',
kweight=KWEIGHT,
group=best,
_larch=mylarch)
xftf(sumgroup.k,
sumgroup.chi,
kmin=KMIN,
kmax=KMAX,
dk=4,
window='hanning',
kweight=KWEIGHT,
group=sumgroup,
_larch=mylarch)
'''chang end'''
exp = g.chi
# params = {}
# params['Kmin'] = Kmin
# params['Kmax'] = Kmax
params['SMALL'] = SMALL
params['BIG'] = BIG
params['intervalK'] = intervalK
return exp, g, params, mylarch
def fitness(exp, arr, paths, params, return_r=True):
base = Path(os.getcwd()).parent.parent
front = os.path.join(base, params['front'])
end = '.dat'
loss = 0
yTotal = [0] * (401)
offset = 2
global best
Kmin = params['Kmin']
Kmax = params['Kmax']
SMALL = params['SMALL']
BIG = params['BIG']
Kweight = params['kweight']
arr_r = []
# print(paths)
array_str = "---------------------\n"
for i in range(len(paths)):
filename = front + str(paths[i]).zfill(4) + end
# print(filename)
path = feffdat.feffpath(filename,
s02=str(arr[i, 0]),
e0=str(arr[i, 1]),
sigma2=str(arr[i, 2]),
deltar=str(arr[i, 3]),
_larch=mylarch)
#print(arr[i-1])
#print(filename)
feffdat.path2chi(path, larch=mylarch)
# print("Path", paths[i], path.s02, path.e0, path.sigma2, path.reff+arr[i,3])
array_str += "Path " + str(paths[i]) + " " + str(path.s02) + " " + str(
path.e0) + " " + str(np.round(float(path.sigma2), 4)) + " " + str(
np.round(path.reff + arr[i, 3], 3)) + "\n"
temp = [
float(path.s02),
float(path.e0),
float(path.sigma2),
float(path.reff + arr[i, 3]),
float(path.degen),
float(path.nleg), (path.geom)
]
arr_r.append(temp)
y = path.chi
for k in intervalK:
yTotal[int(k)] += y[int(k)]
best.chi = yTotal
best.k = path.k
xftf(best.k,
best.chi,
kmin=Kmin,
kmax=Kmax,
dk=4,
window='hanning',
kweight=Kweight,
group=best,
_larch=mylarch)
# offset=-3
# plt.plot(path.k[SMALL:BIG], yTotal[SMALL:BIG]*path.k[SMALL:BIG]**2+offset,label="Total")
# plt.legend(loc='upper left')
for j in intervalK:
#loss = loss + (yTotal[int(j)]*g.k[int(j)]**2 - exp[int(j)]*g.k[int(j)]**2)**2
loss = loss + (yTotal[int(j)] * g.k[int(j)]**2 -
exp[int(j)] * g.k[int(j)]**2)**2
if return_r == True:
return path, yTotal, best, loss, arr_r, array_str
else:
return path, yTotal, best, loss
def fitness_individal(exp, arr, paths, params, plot=False, export=False):
r"""
Fittness for individual score
Inputs:
exp (larch_object): expereince data for larch object
arr (array): array for best fit
path (list): path list
params (dicts): dictionary for params calculations
plot (bool): plot for individual paths
export (bool): return array for each paths
Outputs:
"""
global intervalK
global best
loss = 0
yTotal = [0] * (401)
offset = 5
global best
# print(params)
base = Path(os.getcwd()).parent.parent
front = os.path.join(base, params['front'])
end = '.dat'
Kmax = params['Kmax']
SMALL = params['SMALL']
BIG = params['BIG']
export_paths = np.zeros((2 * len(paths), 401))
for i in range(len(paths)):
filename = front + str(paths[i]).zfill(4) + end
path = feffdat.feffpath(filename,
s02=str(arr[i, 0]),
e0=str(arr[i, 1]),
sigma2=str(arr[i, 2]),
deltar=str(arr[i, 3]),
_larch=mylarch)
feffdat.path2chi(path, _larch=mylarch)
if plot:
plt.figure()
plt.plot(path.k,
path.chi * path.k**2.0 + offset * (i + 1),
label='Path' + str(i))
plt.xlabel("k$^{2}$(Å$^{-1}$)")
plt.ylabel("k$^{2}$\chi(k)(Å$^{-2}$)$")
plt.ylim(-5, len(paths) * offset + offset)
plt.title('Paths: ' + str(i))
# plt.title(str(i+1))
plt.xlim(0, Kmax + 1)
# print(len(paths))
# plt.figure()
# plt.plot(path.k, path.chi*path.k**2.0 + offset*(i+1),label='Path'+str(i))
# plt.xlabel("k$^{2}$(Å$^{-1}$)")
# plt.ylabel("k$^{2}$\chi(k)(Å$^{-2}$)$")
# # plt.ylim(-10,len(paths)*offset+offset)
# plt.xlim(0,Kmax+1)
# plt.show()
if export:
export_paths[2 * i, :] = path.k
export_paths[2 * i + 1, :] = (path.chi * path.k**2.0)
y = path.chi
for k in intervalK:
yTotal[int(k)] += y[int(k)]
best.chi = yTotal
best.k = path.k
xftf(best.k,
best.chi,
kmin=KMIN,
kmax=KMAX,
dk=4,
window='hanning',
kweight=KWEIGHT,
group=best)
offset = 0
if plot:
plt.plot(path.k[SMALL:BIG],
yTotal[SMALL:BIG] * path.k[SMALL:BIG]**2 + offset,
'b',
label="Genetic Algorithm")
# plt.legend('loc = best')
# if export:
# export_paths = export.
for j in intervalK:
#loss = loss + (yTotal[int(j)]*g.k[int(j)]**2 - exp[int(j)]*g.k[int(j)]**2)**2
loss = loss + (yTotal[int(j)] * g.k[int(j)]**2 -
exp[int(j)] * g.k[int(j)]**2)**2
#print(loss)
return path, yTotal, best, loss, export_paths
def read_athena(filename,
match=None,
do_preedge=True,
do_bkg=True,
do_fft=True,
use_hashkey=False,
_larch=None):
"""read athena project file
returns a Group of Groups, one for each Athena Group in the project file
Arguments:
filename (string): name of Athena Project file
match (string): pattern to use to limit imported groups (see Note 1)
do_preedge (bool): whether to do pre-edge subtraction [True]
do_bkg (bool): whether to do XAFS background subtraction [True]
do_fft (bool): whether to do XAFS Fast Fourier transform [True]
use_hashkey (bool): whether to use Athena's hash key as the
group name instead of the Athena label [False]
Returns:
group of groups each named according the label used by Athena.
Notes:
1. To limit the imported groups, use the pattern in `match`,
using '*' to match 'all' '?' to match any single character,
or [sequence] to match any of a sequence of letters. The match
will always be insensitive to case.
3. do_preedge, do_bkg, and do_fft will attempt to reproduce the
pre-edge, background subtraction, and FFT from Athena by using
the parameters saved in the project file.
2. use_hashkey=True will name groups from the internal 5 character
string used by Athena, instead of the group label.
Example:
1. read in all groups from a project file:
cr_data = read_athena('My Cr Project.prj')
2. read in only the "merged" data from a Project, and don't do FFT:
zn_data = read_athena('Zn on Stuff.prj', match='*merge*', do_fft=False)
"""
from larch_plugins.xafs import pre_edge, autobk, xftf
if not os.path.exists(filename):
raise IOError("%s '%s': cannot find file" % (ERR_MSG, filename))
try:
fh = GzipFile(filename)
lines = [bytes2str(t) for t in fh.readlines()]
fh.close()
except:
raise ValueError("%s '%s': invalid gzip file" % (ERR_MSG, filename))
athenagroups = []
dat = {'name': ''}
Athena_version = None
vline = lines.pop(0)
if "Athena project file -- Demeter version" not in vline:
raise ValueError("%s '%s': invalid Athena File" % (ERR_MSG, filename))
major, minor, fix = '0', '0', '0'
try:
vs = vline.split("Athena project file -- Demeter version")[1]
major, minor, fix = vs.split('.')
except:
raise ValueError("%s '%s': cannot read version" % (ERR_MSG, filename))
if int(minor) < 9 or int(fix[:2]) < 21:
raise ValueError("%s '%s': file is too old to read" %
(ERR_MSG, filename))
for t in lines:
if t.startswith('#') or len(t) < 2 or 'undef' in t:
continue
key = t.split(' ')[0].strip()
key = key.replace('$', '').replace('@', '')
if key == 'old_group':
dat['name'] = perl2json(t)
elif key == '[record]':
athenagroups.append(dat)
dat = {'name': ''}
elif key == 'args':
dat['args'] = perl2json(t)
elif key in ('x', 'y', 'i0', 'signal'):
dat[key] = np.array([float(x) for x in perl2json(t)])
if match is not None:
match = match.lower()
out = Group()
out.__doc__ = """XAFS Data from Athena Project File %s""" % (filename)
for dat in athenagroups:
label = dat.get('name', 'unknown')
this = Group(athena_id=label,
energy=dat['x'],
mu=dat['y'],
bkg_params=Group(),
fft_params=Group(),
athena_params=Group())
if 'i0' in dat:
this.i0 = dat['i0']
if 'args' in dat:
for i in range(len(dat['args']) // 2):
key = dat['args'][2 * i]
val = dat['args'][2 * i + 1]
if key.startswith('bkg_'):
setattr(this.bkg_params, key[4:], val)
elif key.startswith('fft_'):
setattr(this.fft_params, key[4:], val)
elif key == 'label':
this.label = val
if not use_hashkey:
label = this.label
else:
setattr(this.athena_params, key, val)
this.__doc__ = """Athena Group Name %s (key='%s')""" % (label,
dat['name'])
olabel = fix_varname(label)
if match is not None:
if not fnmatch(olabel.lower(), match):
continue
if do_preedge or do_bkg:
pars = this.bkg_params
pre_edge(this,
_larch=_larch,
e0=float(pars.e0),
pre1=float(pars.pre1),
pre2=float(pars.pre2),
norm1=float(pars.nor1),
norm2=float(pars.nor2),
nnorm=float(pars.nnorm) - 1,
make_flat=bool(pars.flatten))
if do_bkg and hasattr(pars, 'rbkg'):
autobk(this,
_larch=_larch,
e0=float(pars.e0),
rbkg=float(pars.rbkg),
kmin=float(pars.spl1),
kmax=float(pars.spl2),
kweight=float(pars.kw),
dk=float(pars.dk),
clamp_lo=float(pars.clamp1),
clamp_hi=float(pars.clamp2))
if do_fft:
pars = this.fft_params
kweight = 2
if hasattr(pars, 'kw'):
kweight = float(pars.kw)
xftf(this,
_larch=_larch,
kmin=float(pars.kmin),
kmax=float(pars.kmax),
kweight=kweight,
window=pars.kwindow,
dk=float(pars.dk))
setattr(out, olabel, this)
return out
10067.28 120488.7 89110.0998902 0.30168329
10073.72 118833.7 88265.1000656 0.29738025
10080.18 118434.7 88372.1004302 0.29280544
10086.66 117995.7 88449.0998063 0.28822094
10093.17 118435.7 89180.0997098 0.28371228
10099.69 117303.7 88720.0998253 0.27927983
10106.22 117929.7 89581.1003571 0.27494432
10112.78 116857.7 89144.1003332 0.27070279
10119.36 115791.7 88718.1000129 0.26632896
10125.96 111467.7 85797.099695 0.26174966
10132.57 110079.7 85128.099834 0.25704747
10139.21 104190.7 80953.0999403 0.2523529
10145.86 93726.7 73074.0996945 0.24890911'''
raw_data_lines = raw_data.split('\n')
raw_data_table = []
for line in raw_data_lines:
raw_data_table.append(list(map(lambda x: float(x), line.strip().split())))
table = np.array(raw_data_table)
group.energy = table[:, 0]
group.mu = table[:, 3]
e0 = find_e0(group, _larch=mylarch)
pre_edge(group, _larch=mylarch)
autobk(group, _larch=mylarch)
xftf(group, _larch=mylarch)
xftr(group, _larch=mylarch)
# using "expected names" for XAFS data
rawdata = np.loadtxt('../xafsdata/fe2o3_rt1.xmu')
xafsdat.energy = rawdata[:, 0]
xafsdat.mu = rawdata[:, 1]
xafsdat.i0 = rawdata[:, 1]
# run autobk on the xafsdat Group, including a larch Interpreter....
# note that this expects 'energy' and 'mu' to be in xafsdat, and will
# write data for 'k', 'chi', 'kwin', 'e0', ... into xafsdat
autobk(xafsdat, rbkg=1.0, kweight=2, _larch=my_larch)
# Fourier transform to R space, again passing in a Group (here,
# 'k' and 'chi' are expected, and writitng out 'r', 'chir_mag',
# and so on
xftf(xafsdat, kmin=2, kmax=15, dk=3, kweight=2, _larch=my_larch)
#
# plot grid of results:
# mu + bkg
pylab.subplot(2, 2, 1)
pylab.plot(xafsdat.energy, xafsdat.bkg, 'r--')
pylab.plot(xafsdat.energy, xafsdat.mu)
pylab.xlabel('Energy (eV)')
# normalized XANES
# find array bounds for normalized mu(E) for [e0 - 25: e0 + 75]
j0 = np.abs(xafsdat.energy-(xafsdat.e0 - 25.0)).argmin()
j1 = np.abs(xafsdat.energy-(xafsdat.e0 + 75.0)).argmin()
pylab.subplot(2, 2, 2)
def estimate_noise(k,
chi=None,
group=None,
rmin=15.0,
rmax=30.0,
kweight=1,
kmin=0,
kmax=20,
dk=4,
dk2=None,
kstep=0.05,
kwindow='kaiser',
nfft=2048,
_larch=None,
**kws):
"""
estimate noise levels in EXAFS spectrum and estimate highest k
where data is above the noise level
Parameters:
-----------
k: 1-d array of photo-electron wavenumber in Ang^-1 (or group)
chi: 1-d array of chi
group: output Group [see Note below]
rmin: minimum R value for high-R region of chi(R)
rmax: maximum R value for high-R region of chi(R)
kweight: exponent for weighting spectra by k**kweight [1]
kmin: starting k for FT Window [0]
kmax: ending k for FT Window [20]
dk: tapering parameter for FT Window [4]
dk2: second tapering parameter for FT Window [None]
kstep: value to use for delta_k ( Ang^-1) [0.05]
window: name of window type ['kaiser']
nfft: value to use for N_fft [2048].
Returns:
---------
None -- outputs are written to supplied group. Values (scalars) written
to output group:
epsilon_k estimated noise in chi(k)
epsilon_r estimated noise in chi(R)
kmax_suggest highest estimated k value where |chi(k)| > epsilon_k
Notes:
-------
1. This method uses the high-R portion of chi(R) as a measure of the noise
level in the chi(R) data and uses Parseval's theorem to convert this noise
level to that in chi(k). This method implicitly assumes that there is no
signal in the high-R portion of the spectrum, and that the noise in the
spectrum s "white" (independent of R) . Each of these assumptions can be
questioned.
2. The estimate for 'kmax_suggest' has a tendency to be fair but pessimistic
in how far out the chi(k) data goes before being dominated by noise.
3. Follows the 'First Argument Group' convention, so that you can either
specifiy all of (an array for 'k', an array for 'chi', option output Group)
OR pass a group with 'k' and 'chi' as the first argument
"""
k, chi, group = parse_group_args(k,
members=('k', 'chi'),
defaults=(chi, ),
group=group,
fcn_name='esitmate_noise')
# save _sys.xafsGroup -- we want to NOT write to it here!
savgroup = set_xafsGroup(None, _larch=_larch)
tmpgroup = Group()
rmax_out = min(10 * pi, rmax + 2)
xftf(k,
chi,
kmin=kmin,
kmax=kmax,
rmax_out=rmax_out,
kweight=kweight,
dk=dk,
dk2=dk2,
kwindow=kwindow,
nfft=nfft,
kstep=kstep,
group=tmpgroup,
_larch=_larch)
chir = tmpgroup.chir
rstep = tmpgroup.r[1] - tmpgroup.r[0]
irmin = int(0.01 + rmin / rstep)
irmax = min(nfft / 2, int(1.01 + rmax / rstep))
highr = realimag(chir[irmin:irmax])
# get average of window function value, scale eps_r scale by this
# this is imperfect, but improves the result.
kwin_ave = tmpgroup.kwin.sum() * kstep / (kmax - kmin)
eps_r = sqrt((highr * highr).sum() / len(highr)) / kwin_ave
# use Parseval's theorem to convert epsilon_r to epsilon_k,
# compensating for kweight
w = 2 * kweight + 1
scale = sqrt((2 * pi * w) / (kstep * (kmax**w - kmin**w)))
eps_k = scale * eps_r
# do reverse FT to get chiq array
xftr(tmpgroup.r,
tmpgroup.chir,
group=tmpgroup,
rmin=0.5,
rmax=9.5,
dr=1.0,
window='parzen',
nfft=nfft,
kstep=kstep,
_larch=_larch)
# sets kmax_suggest to the largest k value for which
# | chi(q) / k**kweight| > epsilon_k
iq0 = index_of(tmpgroup.q, (kmax + kmin) / 2.0)
tst = tmpgroup.chiq_mag[iq0:] / (tmpgroup.q[iq0:])**kweight
kmax_suggest = tmpgroup.q[iq0 + where(tst < eps_k)[0][0]]
# restore original _sys.xafsGroup, set output variables
_larch.symtable._sys.xafsGroup = savgroup
group = set_xafsGroup(group, _larch=_larch)
group.epsilon_k = eps_k
group.epsilon_r = eps_r
group.kmax_suggest = kmax_suggest
# save diagrams
# save all as athena project
groups = []
for time_stmp, data_ln in zip(group_vals['time'], group_vals['data']):
new_group = larch.Group()
new_group.mu = data_ln
new_group.energy = group_vals['energy']
# run autobk on the xafsdat Group, including a larch Interpreter....
# note that this expects 'energy' and 'mu' to be in xafsdat, and will
# write data for 'k', 'chi', 'kwin', 'e0', ... into xafsdat
autobk(new_group, rbkg=1.0, kweight=2, _larch=my_larch)
# Fourier transform to R space, again passing in a Group (here,
# 'k' and 'chi' are expected, and writitng out 'r', 'chir_mag',
# and so on
xftf(new_group, kmin=2, kmax=15, dk=3, kweight=2, _larch=my_larch)
new_group.label = f"Reading at {time_stmp}"
# add group to list
groups.append(new_group)
# plot and save each file in group
basic_plot(new_group, save_dir)
# save energy v normalised mu
save_e_nmu(new_group, save_dir)
# merge groups
merged_group = merge_groups(groups)
merged_group.label = "merged"
autobk(merged_group, rbkg=1.0, kweight=2, _larch=my_larch)
def read(self,
filename=None,
match=None,
do_preedge=True,
do_bkg=True,
do_fft=True,
use_hashkey=False):
"""
read Athena project to group of groups, one for each Athena dataset
in the project file. This supports both gzipped and unzipped files
and old-style perl-like project files and new-style JSON project files
Arguments:
filename (string): name of Athena Project file
match (string): pattern to use to limit imported groups (see Note 1)
do_preedge (bool): whether to do pre-edge subtraction [True]
do_bkg (bool): whether to do XAFS background subtraction [True]
do_fft (bool): whether to do XAFS Fast Fourier transform [True]
use_hashkey (bool): whether to use Athena's hash key as the
group name instead of the Athena label [False]
Returns:
None, fills in attributes `header`, `journal`, `filename`, `groups`
Notes:
1. To limit the imported groups, use the pattern in `match`,
using '*' to match 'all', '?' to match any single character,
or [sequence] to match any of a sequence of letters. The match
will always be insensitive to case.
3. do_preedge, do_bkg, and do_fft will attempt to reproduce the
pre-edge, background subtraction, and FFT from Athena by using
the parameters saved in the project file.
2. use_hashkey=True will name groups from the internal 5 character
string used by Athena, instead of the group label.
Example:
1. read in all groups from a project file:
cr_data = read_athena('My Cr Project.prj')
2. read in only the "merged" data from a Project, and don't do FFT:
zn_data = read_athena('Zn on Stuff.prj', match='*merge*', do_fft=False)
"""
if filename is not None:
self.filename = filename
if not os.path.exists(self.filename):
raise IOError("%s '%s': cannot find file" %
(ERR_MSG, self.filename))
from larch_plugins.xafs import pre_edge, autobk, xftf
if not os.path.exists(filename):
raise IOError("file '%s' not found" % filename)
text = _read_raw_athena(filename)
# failed to read:
if text is None:
raise OSError(errval)
if not _test_athena_text(text):
raise ValueError("%s '%s': invalid Athena File" %
(ERR_MSG, filename))
# decode JSON or Perl format
data = None
try:
data = parse_jsonathena(text, self.filename)
except ValueError:
# try as perl format
# print("Not json-athena ", sys.exc_info())
try:
data = parse_perlathena(text, self.filename)
except:
# print("Not perl-athena ", sys.exc_info())
pass
if data is None:
raise ValueError("cannot read file '%s' as Athena Project File" %
(self.filename))
self.header = data.header
self.journal = data.journal
self.group_names = data.group_names
for gname in data.group_names:
oname = gname
if match is not None:
if not fnmatch(gname.lower(), match):
continue
this = getattr(data, gname)
if use_hashkey:
oname = this.athena_id
if (do_preedge or do_bkg) and (self._larch is not None):
pars = this.bkg_params
pre_edge(this,
e0=float(pars.e0),
pre1=float(pars.pre1),
pre2=float(pars.pre2),
norm1=float(pars.nor1),
norm2=float(pars.nor2),
nnorm=float(pars.nnorm),
make_flat=bool(pars.flatten),
_larch=self._larch)
if do_bkg and hasattr(pars, 'rbkg'):
autobk(this,
_larch=self._larch,
e0=float(pars.e0),
rbkg=float(pars.rbkg),
kmin=float(pars.spl1),
kmax=float(pars.spl2),
kweight=float(pars.kw),
dk=float(pars.dk),
clamp_lo=float(pars.clamp1),
clamp_hi=float(pars.clamp2))
if do_fft:
pars = this.fft_params
kweight = 2
if hasattr(pars, 'kw'):
kweight = float(pars.kw)
xftf(this,
_larch=self._larch,
kmin=float(pars.kmin),
kmax=float(pars.kmax),
kweight=kweight,
window=pars.kwindow,
dk=float(pars.dk))
self.groups[oname] = this
def calc_FT(k,chi,kmin_,kmax_,kweight_):
ft = larch_builtins._group(mylarch)
xftf(k,chi,group=ft,kweight=kweight_,kmin=kmin_,kmax=kmax_,_larch=mylarch)
return ft.r, ft.chir_mag, ft.chir_im
def calc_exafs_SplineSmoothing(energy,ut_, E0, fit_s,fit_e,nor_aE0_s,nor_aE0_e,pre_type,degree,kweight,sf):
delta_ut = []
ut_wo_bk = np.array([])
pre_edge = np.array([])
post_edge = np.array([])
i = 1
while i+1 < len(ut_):
delta_ut.append(((ut_[i+1]-ut_[i])/(energy[i+1]-energy[i])+(ut_[i]-ut_[i-1])/(energy[i]-energy[i-1]))/2)
i += 1
delta_ut.append(0.0)
delta_ut.insert(0,0.0)
#find nearest point
startpoint = find_near(energy,fit_s)
endpoint = find_near(energy,fit_e)
print (startpoint)
#print energy[startpoint:endpoint]
if pre_type == 1:
fit_r = np.polyfit(energy[startpoint:endpoint],ut_[startpoint:endpoint],1)
print (fit_r)
pre_edge = fit_r[0]*energy + fit_r[1]
ut_wo_bk = ut_ - pre_edge
elif pre_type == 2:
fit_lin = np.polyfit(energy[startpoint:endpoint],ut_[startpoint:endpoint],1)
def fit_f(x,C,D,Y):
return Y + C/x**3 - D/x**4
E_s_and_e = [energy[startpoint],energy[endpoint]]
ut_s_and_e = [ut_[startpoint],ut_[endpoint]]
X = np.vstack([E_s_and_e ,np.ones(len(E_s_and_e))]).T
DAT = [energy[startpoint]**4*(ut_[startpoint]-fit_lin[1]),energy[endpoint]**4*(ut_[endpoint]-fit_lin[1])]
c, d = linalg.lstsq(X,DAT)[0]
opt, pconv = optim.curve_fit(fit_f,energy[startpoint:endpoint],ut_[startpoint:endpoint],p0=[c,d,fit_lin[1]])
pre_edge = fit_f(energy,opt[0],opt[1],opt[2])
ut_wo_bk = ut_ - pre_edge
elif pre_type == 0:
pre_edge = np.average(ut_[find_near(energy,fit_s):find_near(energy,fit_e)])
ut_wo_bk = ut_ - pre_edge
boundary = [find_near(energy,nor_aE0_s),find_near(energy,nor_aE0_e)]
norm = np.average(ut_wo_bk[find_near(energy,E0+30.0):find_near(energy,E0+80.0)])
k = np.array([])
for e in energy:
if E0 > e:
k = np.append(k,(-1.0)*math.sqrt(0.2626*abs(e-E0)))
else:
k = np.append(k,math.sqrt(0.2626*abs(e-E0)))
num = find_near(energy,E0)
if k[num] < 0:
num += 1
array_weight = None
if kweight != 0:
array_weight = k[num:boundary[1]+1]**kweight
#knots = []
#if num_of_knots == 0:
# pass
#else:
# j = 1
# delta_k = k[-1]/(num_of_knots+1)
# print delta_k
# while j < num_of_knots+1:
# knots.append(k[find_near(k,delta_k*j)])
# j += 1
#knots = [k[find_near(k,2.0)],k[find_near(k,4.0)],k[find_near(k,6)],k[find_near(k,8.0)],k[find_near(k,10.0)],k[find_near(k,12.0)]]
#knots_e = np.array(knots)**2/0.2626 + E0
#print knots
spline = interp.UnivariateSpline(k[num:boundary[1]+1],ut_wo_bk[num:boundary[1]+1]/norm,w = k[num:boundary[1]+1]**kweight, k=degree)
spline_e = interp.UnivariateSpline(energy[num:boundary[1]+1],ut_wo_bk[num:boundary[1]+1]/norm, w = k[num:boundary[1]+1]**kweight, k=degree)
spline.set_smoothing_factor(sf)
post_edge = np.append(ut_wo_bk[:boundary[0]]/norm, spline(k[boundary[0]:boundary[1]+1]))
post_edge = np.append(post_edge, ut_wo_bk[boundary[1]+1:]/norm)
chi = ut_wo_bk/norm - post_edge
bkg = pre_edge + post_edge*norm
k_interp = np.array([])
k_interp = np.append(k_interp,0.0)
while k_interp[-1]-0.05 < k[-1]:
k_interp = np.append(k_interp,k_interp[-1]+0.05)
print (len(k_interp))
chi_interp = np.interp(k_interp,k[num:],chi[num:])
ft = larch_builtins._group(mylarch)
#tft = larch_builtins._group(mylarch)
#chi_q = larch_builtins._group(mylarch)
xftf(k_interp,chi_interp,group=ft,kweight=3,kmin=3.0,kmax=12.0,_larch=mylarch)
post_edge_ = np.append(ut_wo_bk[0:num]/norm,spline(k[num:]))
return bkg, pre_edge, post_edge_*norm+pre_edge, chi_interp, k_interp, ft.r, ft.chir_mag, ft.chir_im, spline.get_knots()
def estimate_noise(k, chi=None, group=None, rmin=15.0, rmax=30.0,
kweight=1, kmin=0, kmax=20, dk=4, dk2=None, kstep=0.05,
kwindow='kaiser', nfft=2048, _larch=None, **kws):
"""
estimate noise levels in EXAFS spectrum and estimate highest k
where data is above the noise level
Parameters:
-----------
k: 1-d array of photo-electron wavenumber in Ang^-1 (or group)
chi: 1-d array of chi
group: output Group [see Note below]
rmin: minimum R value for high-R region of chi(R)
rmax: maximum R value for high-R region of chi(R)
kweight: exponent for weighting spectra by k**kweight [1]
kmin: starting k for FT Window [0]
kmax: ending k for FT Window [20]
dk: tapering parameter for FT Window [4]
dk2: second tapering parameter for FT Window [None]
kstep: value to use for delta_k ( Ang^-1) [0.05]
window: name of window type ['kaiser']
nfft: value to use for N_fft [2048].
Returns:
---------
None -- outputs are written to supplied group. Values (scalars) written
to output group:
epsilon_k estimated noise in chi(k)
epsilon_r estimated noise in chi(R)
kmax_suggest highest estimated k value where |chi(k)| > epsilon_k
Notes:
-------
1. This method uses the high-R portion of chi(R) as a measure of the noise
level in the chi(R) data and uses Parseval's theorem to convert this noise
level to that in chi(k). This method implicitly assumes that there is no
signal in the high-R portion of the spectrum, and that the noise in the
spectrum s "white" (independent of R) . Each of these assumptions can be
questioned.
2. The estimate for 'kmax_suggest' has a tendency to be fair but pessimistic
in how far out the chi(k) data goes before being dominated by noise.
3. Follows the 'First Argument Group' convention, so that you can either
specifiy all of (an array for 'k', an array for 'chi', option output Group)
OR pass a group with 'k' and 'chi' as the first argument
"""
k, chi, group = parse_group_args(k, members=('k', 'chi'),
defaults=(chi,), group=group,
fcn_name='esitmate_noise')
# save _sys.xafsGroup -- we want to NOT write to it here!
savgroup = set_xafsGroup(None, _larch=_larch)
tmpgroup = Group()
rmax_out = min(10*pi, rmax+2)
xftf(k, chi, kmin=kmin, kmax=kmax, rmax_out=rmax_out,
kweight=kweight, dk=dk, dk2=dk2, kwindow=kwindow,
nfft=nfft, kstep=kstep, group=tmpgroup, _larch=_larch)
chir = tmpgroup.chir
rstep = tmpgroup.r[1] - tmpgroup.r[0]
irmin = int(0.01 + rmin/rstep)
irmax = min(nfft/2, int(1.01 + rmax/rstep))
highr = realimag(chir[irmin:irmax])
# get average of window function value, scale eps_r scale by this
# this is imperfect, but improves the result.
kwin_ave = tmpgroup.kwin.sum()*kstep/(kmax-kmin)
eps_r = sqrt((highr*highr).sum() / len(highr)) / kwin_ave
# use Parseval's theorem to convert epsilon_r to epsilon_k,
# compensating for kweight
w = 2 * kweight + 1
scale = sqrt((2*pi*w)/(kstep*(kmax**w - kmin**w)))
eps_k = scale*eps_r
# do reverse FT to get chiq array
xftr(tmpgroup.r, tmpgroup.chir, group=tmpgroup, rmin=0.5, rmax=9.5,
dr=1.0, window='parzen', nfft=nfft, kstep=kstep, _larch=_larch)
# sets kmax_suggest to the largest k value for which
# | chi(q) / k**kweight| > epsilon_k
iq0 = index_of(tmpgroup.q, (kmax+kmin)/2.0)
tst = tmpgroup.chiq_mag[iq0:] / ( tmpgroup.q[iq0:])**kweight
kmax_suggest = tmpgroup.q[iq0 + where(tst < eps_k)[0][0]]
# restore original _sys.xafsGroup, set output variables
_larch.symtable._sys.xafsGroup = savgroup
group = set_xafsGroup(group, _larch=_larch)
group.epsilon_k = eps_k
group.epsilon_r = eps_r
group.kmax_suggest = kmax_suggest
def read(self, filename=None, match=None, do_preedge=True, do_bkg=True,
do_fft=True, use_hashkey=False):
"""
read Athena project to group of groups, one for each Athena dataset
in the project file. This supports both gzipped and unzipped files
and old-style perl-like project files and new-style JSON project files
Arguments:
filename (string): name of Athena Project file
match (string): pattern to use to limit imported groups (see Note 1)
do_preedge (bool): whether to do pre-edge subtraction [True]
do_bkg (bool): whether to do XAFS background subtraction [True]
do_fft (bool): whether to do XAFS Fast Fourier transform [True]
use_hashkey (bool): whether to use Athena's hash key as the
group name instead of the Athena label [False]
Returns:
None, fills in attributes `header`, `journal`, `filename`, `groups`
Notes:
1. To limit the imported groups, use the pattern in `match`,
using '*' to match 'all', '?' to match any single character,
or [sequence] to match any of a sequence of letters. The match
will always be insensitive to case.
3. do_preedge, do_bkg, and do_fft will attempt to reproduce the
pre-edge, background subtraction, and FFT from Athena by using
the parameters saved in the project file.
2. use_hashkey=True will name groups from the internal 5 character
string used by Athena, instead of the group label.
Example:
1. read in all groups from a project file:
cr_data = read_athena('My Cr Project.prj')
2. read in only the "merged" data from a Project, and don't do FFT:
zn_data = read_athena('Zn on Stuff.prj', match='*merge*', do_fft=False)
"""
if filename is not None:
self.filename = filename
if not os.path.exists(self.filename):
raise IOError("%s '%s': cannot find file" % (ERR_MSG, self.filename))
from larch_plugins.xafs import pre_edge, autobk, xftf
if not os.path.exists(filename):
raise IOError("file '%s' not found" % filename)
text = _read_raw_athena(filename)
# failed to read:
if text is None:
raise OSError(errval)
if not _test_athena_text(text):
raise ValueError("%s '%s': invalid Athena File" % (ERR_MSG, filename))
# decode JSON or Perl format
data = None
try:
data = parse_jsonathena(text, self.filename)
except ValueError:
# try as perl format
# print("Not json-athena ", sys.exc_info())
try:
data = parse_perlathena(text, self.filename)
except:
# print("Not perl-athena ", sys.exc_info())
pass
if data is None:
raise ValueError("cannot read file '%s' as Athena Project File" % (self.filename))
self.header = data.header
self.journal = data.journal
self.group_names = data.group_names
for gname in data.group_names:
oname = gname
if match is not None:
if not fnmatch(gname.lower(), match):
continue
this = getattr(data, gname)
if use_hashkey:
oname = this.athena_id
if (do_preedge or do_bkg) and (self._larch is not None):
pars = this.bkg_params
pre_edge(this, e0=float(pars.e0),
pre1=float(pars.pre1), pre2=float(pars.pre2),
norm1=float(pars.nor1), norm2=float(pars.nor2),
nnorm=float(pars.nnorm),
make_flat=bool(pars.flatten), _larch=self._larch)
if do_bkg and hasattr(pars, 'rbkg'):
autobk(this, _larch=self._larch, e0=float(pars.e0),
rbkg=float(pars.rbkg), kmin=float(pars.spl1),
kmax=float(pars.spl2), kweight=float(pars.kw),
dk=float(pars.dk), clamp_lo=float(pars.clamp1),
clamp_hi=float(pars.clamp2))
if do_fft:
pars = this.fft_params
kweight=2
if hasattr(pars, 'kw'):
kweight = float(pars.kw)
xftf(this, _larch=self._larch, kmin=float(pars.kmin),
kmax=float(pars.kmax), kweight=kweight,
window=pars.kwindow, dk=float(pars.dk))
self.groups[oname] = this
